It is of great significance for structural design and engineering evaluation to obtain the elastic–plastic parameters of materials. The inverse estimation of elastic–plastic parameters of materials based on nanoindentation technology has been applied in many pieces of research, but it has proved to be difficult to determine the elastic–plastic properties of materials by only using a single indentation curve. A new optimal inversion strategy based on a spherical indentation curve was proposed to obtain the elastoplastic parameters (the Young’s modulus E, yield strength σ_y, and hardening exponent n) of materials in this study. A high-precision finite element model of indentation with a spherical indenter (radius R = 20 µm) was established, and the relationship between the three parameters and indentation response was analyzed using the design of experiment (DOE) method. The well-posed problem of inverse estimation under different maximum indentation depths (h_max1 = 0.06 R, h_max2 = 0.1 R, h_max3 = 0.2 R, h_max4 = 0.3 R) was explored based on numerical simulations. The results show that the unique solution with high accuracy can be obtained under different maximum press-in depths (the minimum error was within 0.2% and the maximum error was up to 1.5%). Next, the load-depth curves of Q355 were obtained by a cyclic loading nanoindentation experiment, and the elastic–plastic parameters of Q355 were determined by the proposed inverse-estimation strategy based on the average indentation load-depth curve. The results showed that the optimized load-depth curve was in good agreement with the experimental curve, and the optimized stress–strain curve was slightly different from the tensile test, and the obtained parameters were basically consistent with the existing research.

In the context of digital manufacturing and intelligent manufacturing systems, optimizing the inverse kinematics (IK) of manipulators is crucial for improving manufacturing efficiency and productivity. The authors propose an improved particle swarm optimizer (IPSO) with adaptive inertia weight and asynchronous learning factors to enhance the optimization performance. The proposed algorithm introduces a novel mutation strategy where selected initial particles undergo mutation to generate new particles, thereby increasing swarm diversity. The best-performing particles after mutation replace the initial ones to improve global optimization capability. The effectiveness of the IPSO algorithm is evaluated through thirteen classical benchmark functions and compared with genetic algorithms (GA), standard particle swarm optimization (PSO), linear decreasing weight PSO (LDWPSO), and biogeography-based PSO (BLPSO), quantum-behaved particle swarm optimization (QPSO), Levy Flight quantum-behaved particle swarm optimization (LQPSO), Gaussian quantum-behaved particle swarm optimization approaches (GQPSO). Experimental mean results demonstrate that the IPSO algorithm outperforms these algorithms. The IPSO algorithm and the other algorithms subsequently were employed to estimate the performance for tackling the IK problem of a manipulator and the comparison results demonstrate the superior performance of IPSO algorithm in terms of accuracy and convergence rate. Finally, the practical efficacy of the IPSO algorithm was validated through experimental trials conducted on a custom-designed six-degree-of-freedom (6-DOF) collaborative robotic manipulator platform, which demonstrated the viability of IPSO algorithm in real-world manufacturing applications. The findings suggest that the proposed IPSO algorithm offers a promising solution for enhancing manipulator control efficiency in digital manufacturing systems. The IPSO code is available at: https://10.6084/m9.figshare.27753549 .

Aiming at the problems of complex and slow optimization process of large-scale wind turbine blade design and the influence of wind turbine flutter, etc. The numerical simulation analysis and modal analysis of the external flow field of the wind turbine under the rated wind speed are carried out by the hybrid method. A fluid simulation model based on CFD was established. The method of rotating reference frame and SST k-omega turbulence model are used to simulate the external flow of fan wind turbine in FLUENT. The first 6 natural modes of the wind wheel are calculated in Hypermesh/Optistruct. The results show that the hybrid simulation method can quickly obtain the impeller flow field information, and the flow of the tip portion is extremely unstable. In each natural mode, the blade deformation position is basically from the maximum chord length to the tip position. The closer to the tip, the more obvious the blade vibration deformation is. At the same time, the first-order natural frequency of the wind wheel is 0.919Hz, which is greater than 0.382Hz when the rated speed is 12m/s, thus effectively avoiding resonance phenomenon.